{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/data/khuangaf/miniconda2/envs/py35/lib/python3.5/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import os\n",
    "import pandas as pd\n",
    "# import matplotlib\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with h5py.File(''.join(['data/bitcoin2015to2017_close.h5']), 'r') as hf:\n",
    "    datas = hf['inputs'].value\n",
    "    labels = hf['outputs'].value\n",
    "    input_times = hf['input_times'].value\n",
    "    output_times = hf['output_times'].value\n",
    "    original_inputs = hf['original_inputs'].value\n",
    "    original_outputs = hf['original_outputs'].value\n",
    "    original_datas = hf['original_datas'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "scaler=MinMaxScaler()\n",
    "#split training validation\n",
    "training_size = int(0.8* datas.shape[0])\n",
    "training_datas = datas[:training_size,:,:]\n",
    "training_labels = labels[:training_size,:,0]\n",
    "validation_datas = datas[training_size:,:,:]\n",
    "validation_labels = labels[training_size:,:,0]\n",
    "validation_original_outputs = original_outputs[training_size:,:,:]\n",
    "validation_original_inputs = original_inputs[training_size:,:,:]\n",
    "validation_input_times = input_times[training_size:,:,:]\n",
    "validation_output_times = output_times[training_size:,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(221, 272, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true = np.append(validation_original_inputs,validation_original_outputs, axis=1)\n",
    "ground_true.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(221, 272, 1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_times = np.append(validation_input_times,validation_output_times, axis=1)\n",
    "ground_true_times.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((884, 256, 1), (884, 16))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_datas.shape, training_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(221, 16)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted = np.zeros(validation_labels.shape)\n",
    "for i in range(16):\n",
    "    lr = LinearRegression()\n",
    "    lr.fit(training_datas[:,:,0], training_labels[:,i])\n",
    "    predicted[:,i] = lr.predict(validation_datas[:,:,0])\n",
    "predicted.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 221, 16)\n",
      "(3536,)\n"
     ]
    }
   ],
   "source": [
    "predicted_inverted = []\n",
    "\n",
    "scaler.fit(original_datas[:,0].reshape(-1,1))\n",
    "predicted_inverted.append(scaler.inverse_transform(predicted))\n",
    "print (np.array(predicted_inverted).shape)\n",
    "#get only the close data\n",
    "ground_true = ground_true[:,:,0].reshape(-1)\n",
    "ground_true_times = ground_true_times.reshape(-1)\n",
    "ground_true_times = pd.to_datetime(ground_true_times, unit='s')\n",
    "# since we are appending in the first dimension\n",
    "predicted_inverted = np.array(predicted_inverted)[0,:,:].reshape(-1)\n",
    "print (np.array(predicted_inverted).shape)\n",
    "validation_output_times = pd.to_datetime(validation_output_times.reshape(-1), unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60112, 2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_df = pd.DataFrame()\n",
    "ground_true_df['times'] = ground_true_times\n",
    "ground_true_df['value'] = ground_true\n",
    "ground_true_df.set_index('times').reset_index()\n",
    "ground_true_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3536, 2)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_df = pd.DataFrame()\n",
    "prediction_df['times'] = validation_output_times\n",
    "prediction_df['value'] = predicted_inverted\n",
    "prediction_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction_df = prediction_df.loc[(prediction_df[\"times\"].dt.year == 2017 )&(prediction_df[\"times\"].dt.month > 7 ),: ]\n",
    "ground_true_df = ground_true_df.loc[(ground_true_df[\"times\"].dt.year == 2017 )&(ground_true_df[\"times\"].dt.month > 7 ),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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rcKimqL9UURE4938rt8WrvLhqawunwYd314Vl/VXSOz+xBbXD7176TJKU24bG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\nF0wKHG+eO0ma+aJq8nqo98E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L03TE0NS49g1A38DYQwAAAABAq/x87di6tXX7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\nVGVJ1imAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5929b88fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(ground_true_df.times,ground_true_df.value, label = 'Actual')\n",
    "# plt.plot(prediction_df.times,prediction_df.value, label = 'Predicted')\n",
    "plt.plot(prediction_df.times,prediction_df.value,'ro', label='Predicted')\n",
    "plt.legend(loc='upper left')\n",
    "plt.savefig('result/bitcoin2015to2017_close_lr.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19789.368785630475"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(validation_original_outputs[:,:,0].reshape(-1),predicted_inverted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
